2021.8.10 RESIDENTIAL Electricity consumption ANALYSIS.pdf (2.8 MB)
RESIDENTIAL ELECTRICITY CONSUMPTION ANALYSIS: A CROSSDOMAIN APPROACH TO EVALUATE THE IMPACT OF COVID-19 IN A RESIDENTIAL AREA IN INDIANA
thesis
posted on 2021-08-10, 20:21 authored by Manuel Eduardo Mar ValenciaManuel Eduardo Mar ValenciaThe pandemic scenario caused by COVID-19 is an event with no precedent. Therefore, it
is a phenomenon that can be studied to observe how electricity loads have changed during the stayat-home order weeks. The data collection process was done through online surveys and using
publicly available data. This study is focusing on analyzing household energy units such as
appliances, HVAC, lighting systems. However, collecting this data is expensive and timeconsuming since dwellings would have to be studied individually. As a solution, previous studies
have shown success in characterizing residential electricity using surveys with stochastic models.
This characterized electricity consumption data allows the researchers to generate a predictive
model, make a regression and understand the data. In that way, the data collection process will not
be as costly as installing measuring instruments or smart meters. The input data will be the
behavioral characteristics of each participant; meanwhile, the output of the analysis will be the
estimated electricity consumption "kWh." After generating the "kWh" target, a sensitivity analysis
will be done to observe the electricity consumption through time and examine how people evolved
their load during and after the stay-at-home order.
This research can help understand the change in electricity consumption of people who
worked at home during the pandemic and generate energy indicators and costs such as home office
electricity cost kWh/year. In addition to utilities and energy, managers can benefit from having a
clear understanding of domestic consumers during emergency scenarios as pandemics.
is a phenomenon that can be studied to observe how electricity loads have changed during the stayat-home order weeks. The data collection process was done through online surveys and using
publicly available data. This study is focusing on analyzing household energy units such as
appliances, HVAC, lighting systems. However, collecting this data is expensive and timeconsuming since dwellings would have to be studied individually. As a solution, previous studies
have shown success in characterizing residential electricity using surveys with stochastic models.
This characterized electricity consumption data allows the researchers to generate a predictive
model, make a regression and understand the data. In that way, the data collection process will not
be as costly as installing measuring instruments or smart meters. The input data will be the
behavioral characteristics of each participant; meanwhile, the output of the analysis will be the
estimated electricity consumption "kWh." After generating the "kWh" target, a sensitivity analysis
will be done to observe the electricity consumption through time and examine how people evolved
their load during and after the stay-at-home order.
This research can help understand the change in electricity consumption of people who
worked at home during the pandemic and generate energy indicators and costs such as home office
electricity cost kWh/year. In addition to utilities and energy, managers can benefit from having a
clear understanding of domestic consumers during emergency scenarios as pandemics.
History
Degree Type
- Master of Science
Department
- Computer and Information Technology
Campus location
- West Lafayette
